A Modified Viterbi Algorithm-Based IF Estimation Algorithm for Adaptive Directional Time–Frequency Distributions

Khan, Nabeel Ali; Mohammadi, Mokhtar; Djurović, Igor

doi:10.1007/s00034-018-0960-z

Cited by 42 publications

(21 citation statements)

References 29 publications

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“…The latter is usually performed by some modifications of the Viterbi Algorithm, which is designed to find the optimal path through minimization of a proper penalty function. In order to avoid the socalled "switch problem, " that is the assignment of a ridge point to the wrong component, the penalty functions are defined to promote the local direction of the estimated ridge [28] as well as to impose the local monotonicity [29]. A similar peak-detection and tracking procedure promoting the direction of the estimated IF is proposed in [30].…”

Section: Brief State Of the Artmentioning

confidence: 99%

“…In order to compare the proposed procedure with some of the state of the art methods, we considered the signal v(t) = e i2π(21t 3 ) + e i2π(45t−15t 3 ) + e i2π(62t−15t 3 ) , (25) composed of two parallel non-LFM components intersecting with a non-LFM mode. The sampling rate is 256 Hz and the signal duration is 1 s. We compared our result with the one obtained by the modified Viterbi-ADTFD method [28], which combines adaptive TF analysis and a modified version of Viterbi algorithm. Spectrogram is shown in Fig.…”

Section: Comparative Studiesmentioning

confidence: 99%

“…To quantify this sensitiveness, the same figure shows MSE results provided by the proposed method when boundary points are omitted in the evaluation of the metric (star markers). It is also worth pointing out that, as the majority of tracking peak-based methods, the procedure introduced in [28] is based on a local estimate of IF that is used to adapt the kernel of the TFD. Furthermore, Modified Viterbi-ADTFD method is a searching technique and its performance strongly depends on the predefined set of candidate parameters.…”

Section: Comparative Studiesmentioning

confidence: 99%

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Radon spectrogram-based approach for automatic IFs separation

Bruni

Tartaglione

Vitulano

2020

EURASIP J. Adv. Signal Process.

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The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In order to estimate the instantaneous frequencies of a multicomponent signal, it is necessary to disentangle signal modes in a proper domain. Unfortunately, if signal modes supports overlap both in time and frequency, separation is only possible through a parametric approach whenever the signal class is a priori fixed. In this work, time-frequency analysis and Radon transform are jointly used for the unsupervised separation of modes of a generic frequency modulated signal in noisy environment. The proposed method takes advantage of the ability of the Radon transform of a proper time-frequency distribution in separating overlapping modes. It consists of a blind segmentation of signal components in Radon domain by means of a near-to-optimal threshold operation. The inversion of the Radon transform on each detected region allows us to isolate the instantaneous frequency curves of each single mode in the time-frequency domain. Experimental results performed on constant amplitudes chirp signals confirm the effectiveness of the proposed method, opening the way for its extension to more complex frequency modulated signals.

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Section: Brief State Of the Artmentioning

confidence: 99%

Section: Comparative Studiesmentioning

confidence: 99%

Section: Comparative Studiesmentioning

confidence: 99%

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Radon spectrogram-based approach for automatic IFs separation

Bruni

Tartaglione

Vitulano

2020

EURASIP J. Adv. Signal Process.

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“…In this framework, some recently published works address this issue by the introduction of a generalized Viterbi algorithm. The latter provides an optimization step aimed at promoting the direction of the estimated IF curve [38,39], or its local monotonicity [40]. A similar peaks-detection and tracking procedure can be found in [41].…”

Section: Introductionmentioning

confidence: 99%

A pde-Based Analysis of the Spectrogram Image for Instantaneous Frequency Estimation

2021

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Instantaneous frequency (IF) is a fundamental feature in multicomponent signals analysis and its estimation is required in many practical applications. This goal can be successfully reached for well separated components, while it still is an open problem in case of interfering modes. Most of the methods addressing this issue are parametric, that is, they apply to a specific IF class. Alternative approaches consist of non-parametric time filtering-based procedures, which do not show robustness to destructive interference—the most critical scenario in crossing modes. In this paper, a method for IF curves estimation is proposed. The case of amplitude and frequency modulated two-component signals is addressed by introducing a spectrogram time-frequency evolution law, whose coefficients depend on signal IFs time derivatives, that is, the chirp rates. The problem is then turned into the resolution of a two-dimensional linear system which provides signal chirp rates; IF curves are then obtained by a simple integration. The method is non-parametric and it results quite robust to destructive interference. An estimate of the estimation error, as well as a numerical study concerning method sensitivity and robustness to noise are also provided in the paper.

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“…Since modes' number is unknown, the decomposition task is sometimes performed iteratively, according to some energy balance-based stopping criteria [20]. However, most methods require prior knowledge of the number of signal components [11,[21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

2020

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Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.

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A Modified Viterbi Algorithm-Based IF Estimation Algorithm for Adaptive Directional Time–Frequency Distributions

Cited by 42 publications

References 29 publications

Radon spectrogram-based approach for automatic IFs separation

Radon spectrogram-based approach for automatic IFs separation

A pde-Based Analysis of the Spectrogram Image for Instantaneous Frequency Estimation

A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

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